1. Field of the Invention
The present subject matter relates generally to machine learning and more specifically to efficient processing of data sets generated by applications providing auto-adaptive functions in an auto-adaptive network to locate and extract events and analyze risks.
2. Related Art
Auto-adaptive systems have many applications. These applications include event recognition based on data measured over a number of successive time periods. Events take many different forms. For example, events may include detection of a target in a particular area, sensing of an out-of-specification condition in a physical process environment or correspondence of processed psychometric measurements with a particular behavior prediction profile. Anomaly sensing is often an element of detecting an event. Event recognition may also comprise evaluation of sensed data to recognize or reject existence of conditions indicated by the data or to initiate a particular action.
One use of event detection is in military operations. When making critical combat decisions, a commander must often decide to either act at once or hold off and get more information. Immediate action may offer tactical advantages and improve success prospects, but it could also lead to heavy losses. Getting more data may improve situational awareness and avoid heavy losses, but resulting delays may cause other problems. Making the right choice depends strongly on knowing how much could be gained from gathering more information, and how much could be lost by delaying action.
In conventional solutions, data is collected in the field by sensors of one kind or another. In the context of the present description, a sensor is an item that provides information that may be used to produce a meaningful result. Data is collected over successive time periods, generally from an array of sensors. Depending on the conditions being analyzed and the type of sensors utilized, different types of data points may be established. For example, a data point characterizing a position of a point in a plane may be characterized by x and y coordinates. Such a point has two spatial dimensions. Other dimensions may also exist. For example, if the data point describes the condition of a pixel in a television display, the data point may be further characterized by values of luminance and chroma. These values are characterized as further dimensions of a data point.
In order to describe an environment mathematically, event recognition adaptive algorithms process successive signals in one or a plurality of dimensions to converge on a model of the background environment to track the background's dynamic change. Systems employing such algorithms are referred to as machine learning and can be implemented in parallel configurations to allow for extra speed afforded by parallel processing. When an event occurs within a sensor's area of response, e.g., within a field of view of optical sensors, the adaptive algorithms determine if the return is sufficiently different from the background prediction. Domain specific event identification algorithms may then be applied to verify if an event has occurred in order to minimize the likelihood and number of false positives.
An important aspect of the adaptive algorithm approach is a dynamic detection threshold that enables these systems to find signals and events that could otherwise be lost in noise were they to be compared to fixed thresholds. Having a dynamic threshold also allows a system to maintain a tighter range on alarm limits. Broader alarm ranges decrease the ability of the system to distinguish anomalous conditions from normal conditions.
These conventional complex event detection systems have many known drawbacks and require powerful processors rather than the simpler, less expensive field programmable gating arrays (“FPGAs”) that are desirable for field deployment. Additionally, many conventional complex event detection systems use C++ programming, which is effective but slow in comparison to the simple instructions used for FPGAs. However, as new unmanned vehicles are being developed that are smaller, more agile, and have the capability of reaching places that have not been reached before, the demands made upon the data processing capabilities of these conventional systems have increased dramatically.
Conventional complex event detection systems also lack efficient ways of handling large arrays of data. In many applications, processor in the field will need to respond to large data sets output from a large number of sensors. The sensors will be producing consecutive outputs at a high frequency. Conventional systems process these data sets using the inverse of a covariance matrix, which is a highly complex calculation, especially when the number of covariates is large. Additionally, these conventional complex event detection systems are designed to handle event detection and adaptive learning after entire sets of data have been collected, which is extremely inefficient and undesirable in field deployed applications. Furthermore, conventional systems fail to incorporate risk analysis when processing data sets in the field. Accordingly, what is needed is a system and method that overcomes these significant problems found in the conventional systems as described above.